• 인터넷정보학회논문지
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• 제9권3호
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• pp.103-117
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• 2008

일반 고객이 대기업 혹은 중소기업, 증권, 금융, 은행권 등의 서비스를 제공받기 위해서는 보편적으로 인터넷, SMS(Short Message Service), ATM(Automated Teller Machine), DM(Direct Mail), 텔레포니 서비스 등을 사용한다. 특히, 화재 보험사 및 증권, 금융, 은행권의 경우는 QoS(Quality of Service) 보장을 통한 실시간성 제약 조건을 만족해야 한다. 본 논문에서는 고객의 최초 접점으로 이용될 수 있는 CRM(Customer Relationship Management) 환경 하에서 다중 채널 서비스를 위한 결함허용 IVR(Interactive Voice Response)을 설계 구현하였다. 제안한 모델은 대 고객 응대 CRM 모델로 효율적으로 이용될 것으로 사료된다.

• 한국응급구조학회지
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• 제17권3호
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• pp.53-60
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• 2013

Purpose: The purpose of the study is to emphasize the importance of out-of-hospital cardiac arrest resuscitation. This resuscitation by paramedic is very effectively performed under the medical direction of the doctors. Methods: The cardiac arrest victim was 4 month old infant. Informed consent from the parents of the infant was received. CPR combined with 150J defibrillation was performed to the 4 month old infant. Results: We reported that the 4 month old infant survived the cardiac arrest. Out-of-hospital cardiac arrest infant survived after 150J automated external defibrillator and CPR performance. Conclusion: Specific operative protocol is important because the paramedic can apply the proper manual defibrillator effectively. It is important to extend the work scpoe of the EMT.

• 한국통신학회논문지
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• 제42권4호
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• pp.904-914
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• 2017

효율적인 에너지의 사용을 위해 수요반응이라는 개념이 등장하였고 지능화 된 수요반응 서비스를 제공하기 위한 Open Automated Demand Response(OpenADR) 표준 프로토콜이 개발되었다. 최근 스마트 홈 중심의 에너지 Internet of Things (IoT) 분야에서도 사물인터넷 기술을 이용하여 다수의 스마트 홈 기기들에 수요반응 및 에너지 관리 서비스를 제공하려는 시도가 늘어나고 있다. 그러나 스마트 홈 에너지 IoT 환경에서는 많은 수의 초경량 디바이스들이 연결되기 때문에 기존의 HTTP/XML 기반의 OpenADR 수요반응 프로토콜보다 경량의 메시지를 이용한 수요반응 프로토콜이 필요하다. 본 논문에서는 Smart Energy IoT 환경에서 수요반응 서비스를 제공하기 위한 경량의 CoAP/JSON 프로토콜에 기반 한 경량화된 OpenADR 프로토콜을 제안하고 기존의 HTTP/XML 형식의 프로토콜과 성능을 비교 및 검증하였다.

• 한국응급구조학회지
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• 제21권2호
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• pp.39-50
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• 2017

Purpose: This study measured awareness of automated external defibrillators (AED) among students majoring in health-related versus non-health-related fields. Methods: A total of 577 students filled out a questionnaire on awareness of AEDs between June 12 and June 28, 2017. Using SPSS 23.0, data were analyzed using descriptive statistics, chi-square tests, and t-tests. Results: In response to a question about awareness of AED, 25.0% of students majoring in a health-related and 38.6% of those majoring in a non-health-related field answered on "I have seen or heard" and "I have no idea". In response to a question on perception on AED use, 82.4% of students majoring in a health-related field and 88.1% of those in a non-health-related field reported that they thought perception of AED use was not universal. In terms of experience with education on the use of AED, 30.2% of health-related majors and 49.7% of non-health-related majors had not received any training on the use of AED. The average overall score regarding knowledge about AED was 8.69 for health-related majors, compared to 7.79 for non-health-related majors. Conclusion: In order to improve awareness regarding AED use, education on importance and necessity of AED should be emphasized and implemented consistently by the mass media.

• 한국IT서비스학회지
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• 제18권2호
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.1010-1032
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• 2018

Recent advances in medical science have made people live longer, which has affected many aspects of life, such as caregiver burden, increasing cost of healthcare, increasing number of disabled and depressive disorder persons, and so on. Researchers are now focused on elderly living assistance services in smart home environments. In recent years, assisted living technologies have rapidly grown due to a faster growing aging society. Many smart devices are now interconnected within the home network environment and such a home setup supports collaborations between those devices based on the Internet of Things (IoT). One of the major challenges in providing elderly living assistance services is to consider each individual's requirements of different needs. In order to solve this, the virtualization of physical things, as well as the collaboration and composition of services provided by these physical things should be considered. In order to meet these challenges, Web of Objects (WoO) focuses on the implementation aspects of IoT to bring the assorted real world objects with the web applications. We proposed a semantic modelling technique for manual and semi-automated service composition. The aim of this work is to propose a framework to enable RESTful web services composition using semantic ontology for elderly living assistance services creation in WoO based smart home environment.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.32-42
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• 2023

The paper describes the new approaches to the automated analysis of curricula according to the higher education standard. The analysis process is proposed to carry out in two ways: (a) the analysis of completeness and sufficiency of curricula according to the standard of higher education; (b) the comparison of curricula of the same qualification and specialty. The problem of improving the quality of university students' training launches the process of monitoring and analyzing educational curricula and their correspondence to the higher education standard. We developed the rules and methods to compare curricula. In addition, we implemented the automated system of curricula comparison. The paper reveals the use of these methods based on the analysis of the curriculum bachelor level of higher education "Informatics", specialty "Computer science", at the Faculty of Computer Science and Cybernetics of the Taras Shevchenko National University of Kyiv. The findings put towards the idea that the implementation of developed methods as well as the automated system of curricula analysis will improve the educational services by higher education institutions.

• International Journal of Contents
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• 제6권4호
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• pp.8-13
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• 2010

Due to the expense of health care and the need to contain costs, many stroke patients are discharged from hospitals while still in an impaired condition. Using Tele-rehabilitation, these patients can receive rehabilitation services remotely. A pegboard is a conventional rehabilitation therapeutic device that integrates cognition, sensation and hand motor function. This study proposes a Tele-rehabilitation content with automated pegboard and shows its functional feasibility. The evaluation of the pegboard session was automated with RFID (radio frequency identification), and a 16-hole pegboard was rapid-prototyped. After a pegboard session is completed, the session result is uploaded to a server automatically for viewing on a web browser by a remote therapist. The therapist can also send messages to remote patients to encourage them or to manage the rehabilitation process.

• 한국정밀공학회지
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• 제16권9호
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• pp.25-34
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• 1999

In spite of the advance of 3D solid modeling technology, there are some distinct areas where 2D CAD S/W are still dominant, and more competent comparing with 3D CAD S/W. For example, in the manufacturing of 2D-shaped electrical parts, most related manufacturing tools have 2D geometric features by nature, and 3D solid models applied to these parts have substantial overheads. Nevertheless, most 2D CAD S/W have no topological inquiry services because they have no such information on their geometrical database inherently. Thus, it is needed to extract such information from 2D CAD database for developing more advanced application such as automated drafting/design S/W. In this paper, the extraction of topological information from 2D CAD has been performed in general way using concept of B-rep. A general extraction algorithm, data structure and meta file format for 2D topological object have been developed and successfully applied to the development of the automated lead frame die design system in Samsung Aerospace. it is also possible to provide a flexible, powerful topology-oriented functionality on any common 2D CAD S/W.